Biomedical data are quasi-periodic, which means that patterns of biological activity are repetitive to some extent, but each cycle of repetition is slightly different. Some examples of repetitive patterns include heart beats and associated electrocardiographic (ECG) waveforms, which consist of a sequence of waveforms, referred to as the P, Q, R, S, and T-waves. Yet, the duration and amplitudes of these waveforms change from beat to beat, and certain types of changes signify development of a heart disease or non-cardiac physiological disorder. An accurate analysis of changes in the amplitudes and durations of the ECG waveforms is important for medical diagnosis, detecting changes in someone's health or fitness level. Therefore, analysis of these ECG waveforms is the 1st step of a standard diagnostic ECG analysis, which is commonly used in clinical practice. A number of algorithms have been developed for the detection and classification of such ECG waveforms in the diagnostic 12-lead ECG, ambulatory (Holter) recordings, telemetry recordings or implantable devices, using various filters, template matching, wavelet transform, Markov, hidden Markov models, and neural networks. However, analysis of serial changes in such waveforms remains challenging due to the following reasons. First, the changes may be slow or gradual, escaping detection by traditional, visual analysis or simple statistical tests (such as a simple comparison with a threshold value). Second, many individuals already have pre-existing abnormalities in their medical data. For example, patients with chronic heart disease often have a persistent deviation of the level of electrocardiographic ST-segment from the isoelectric line. This makes identification of new, more recent changes in the ST-segment difficult in such patients, and simple statistical tests (for example, comparison with an average threshold value) may not detect acute changes masked by pre-existing, abnormal patterns. Finally, the identification and classification of physiological patterns is obscured by a large number of external (environmental) and internal (physiological, biochemical, biophysical, genetic, etc.) modulating factors.
Other types of repetitive patterns which are important for medical diagnosis and patient management include: i) respiratory movements or breathing patterns, which exhibit a variety of changes during physical or mental stress, and may become irregular during sleep (also referred to as the sleep disordered breathing or sleep apnea); ii) circadian (day-night) variations in physiological activity, including physical activity, metabolism, heart rate and blood pressure; such circadian rhythmicity has been observed in a number of clinical events and complications, for example, the morning rise in the incidence of myocardial infarction, sudden death and ventricular tachyarrhythmias, in contrast to the nocturnal rise in the incidence of paroxysmal atrial fibrillation; iii) seasonal variations in the incidence, severity and complications of chronic diseases (for example, an increase in the number of complications associated with the duodenal ulcers in the spring and fall seasons).
Another example of a repetitive signal is beat-to-beat alterations in the amplitude of the T-wave (referred to as the T-wave alternans or TWA), which may indicate heightened risk of sudden death. TWA are also affected by changes in heart rate, physiological and neurohormonal activity, respiration and other modulating factors, which obscure their accurate analysis.
Tracking changes in health or medical data, using individual's own data as a personalized reference, allows one to improve the accuracy of medical diagnosis. Comparing current data with individual's historical test results, such as previous electrocardiogram (ECG), blood pressure, heart rate, cardiac output, intra-thoracic fluid or transthoracic impedance, helps physicians in differentiating acute changes, which usually require proactive management, from chronic abnormalities. In addition, comparison with individual's historical data also helps in exposing subtle or gradual changes. For example, patients with chronic ischemic heart disease often have gradual narrowing of coronary arteries, which is associated with gradual, subtle changes in the electrocardiographic STT-complex, which are difficult to detect. Other symptoms may include slowly diminishing tolerance to physical exercise, which can also be difficult to detect. In the absence of individual's historical data, physicians often rely on population-derived statistical averages, which may not be applicable to a particular individual and may lead to incomplete or inaccurate diagnosis.